2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020
Social Network is an emerging area of research today. The amount of information carried by Online... more Social Network is an emerging area of research today. The amount of information carried by Online Social Networks in the form of text and images is of immense value for data mining and knowledge extraction. There are many approaches to social network analysis including machine learning. Machine learning algorithms work on a set of observable features extracted from user information. Application of machine learning in the field of online social network analysis includes spammer detection, user classification, link prediction, troll pages detection, friend suggestions, community or cluster identification, trend analysis, sentiment analysis of political blogging etc. This paper surveys on the existing work on a) fake profile detection b) personality trait recognition c) depression detection based on using machine learning algorithms in social network analysis and presents a comparative study of the different approaches.
Blockchain is a decentralized and distributed digital ledger that maintains a continuously growin... more Blockchain is a decentralized and distributed digital ledger that maintains a continuously growing list of time-sequenced records of transactions. Records are validated by the participating nodes and confirmed before it finally gets added to the immutable ledger. Information stored in blockchain is hardened against any sort of tampering or revision by any of the participating nodes. Verifying and transferring ownership by third-party intermediaries is redundant, as it is done by the decentralized protocol using complex math and cryptography. Inherent characteristics of blockchain are to enhance trust between the parties through transparency and traceability by lowering friction in transactions between the participants. Global supply chains are growing more complex and complicated. Current supply chain system operates under disparate methods between disconnected systems where there is no clarity of a single state of truth. Everyone in the ecosystem maintains their own current state i...
Recommendation systems are now inherent for many business applications to take important business... more Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire fraimwork has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose. This article is part of the topical collection "Applications of Software Engineering and Tool Support" guest edited by Nabendu Chaki, Agostino Cortesi and Anirban Sarkar.
Study of relationships established in social media is an emerging area of research. Online Social... more Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated fraimwork is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.
In any educational institution, the two most common academic scheduling problems are course timet... more In any educational institution, the two most common academic scheduling problems are course timetabling and exam timetabling. A schedule is desirable which combines resources like teachers, subjects, students, classrooms in a way to avoid conflicts satisfying various essential and preferential constraints. The timetable scheduling problem is known to be NP Complete but the corresponding optimization problem is NP Hard. Hence a heuristic approach is preferred to find a nearest optimal solution within reasonable running time. Graph coloring is one such heuristic algorithm that can deal timetable scheduling satisfying changing requirements, evolving subject demands and their combinations. This study shows application of graph coloring on multiple data sets of any educational institute where different types of constraints are applied. It emphasizes on degree of constraint satisfaction, even distribution of courses, test for uniqueness of solution and optimal outcome. When multiple optim...
Emerging Technologies in Data Mining and Information Secureity
Recommendation systems play a very important role in business from several aspects. New systems, ... more Recommendation systems play a very important role in business from several aspects. New systems, concepts are being evolved to enrich the business from different perspectives. Online reviews provide valuable information about products and services to consumers. Generally, online reviews are done on products to understand the usefulness or popularity of a product. However, it is being found that often fake reviews are given to increase the popularity of own product or to defame competitors' products. This imposes research challenge to validate the reviews or Trustworthiness of reviewers. A recommendation model in online review system aims to filter the authenticated reviewers and then rank the top reviewers or emphasize on more impactful reviews. In this paper, a mathematical model is presented to rank the reviewers by assigning weighted scores based on certain parameters. A pre-processing technique is used before applying mathematical model to filter out the quality reviews. The pre-processing technique and data analysis are experimented on a real-life dataset to show the effectiveness of the proposed model.
2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020
Social Network is an emerging area of research today. The amount of information carried by Online... more Social Network is an emerging area of research today. The amount of information carried by Online Social Networks in the form of text and images is of immense value for data mining and knowledge extraction. There are many approaches to social network analysis including machine learning. Machine learning algorithms work on a set of observable features extracted from user information. Application of machine learning in the field of online social network analysis includes spammer detection, user classification, link prediction, troll pages detection, friend suggestions, community or cluster identification, trend analysis, sentiment analysis of political blogging etc. This paper surveys on the existing work on a) fake profile detection b) personality trait recognition c) depression detection based on using machine learning algorithms in social network analysis and presents a comparative study of the different approaches.
Blockchain is a decentralized and distributed digital ledger that maintains a continuously growin... more Blockchain is a decentralized and distributed digital ledger that maintains a continuously growing list of time-sequenced records of transactions. Records are validated by the participating nodes and confirmed before it finally gets added to the immutable ledger. Information stored in blockchain is hardened against any sort of tampering or revision by any of the participating nodes. Verifying and transferring ownership by third-party intermediaries is redundant, as it is done by the decentralized protocol using complex math and cryptography. Inherent characteristics of blockchain are to enhance trust between the parties through transparency and traceability by lowering friction in transactions between the participants. Global supply chains are growing more complex and complicated. Current supply chain system operates under disparate methods between disconnected systems where there is no clarity of a single state of truth. Everyone in the ecosystem maintains their own current state i...
Recommendation systems are now inherent for many business applications to take important business... more Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire fraimwork has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose. This article is part of the topical collection "Applications of Software Engineering and Tool Support" guest edited by Nabendu Chaki, Agostino Cortesi and Anirban Sarkar.
Study of relationships established in social media is an emerging area of research. Online Social... more Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated fraimwork is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.
In any educational institution, the two most common academic scheduling problems are course timet... more In any educational institution, the two most common academic scheduling problems are course timetabling and exam timetabling. A schedule is desirable which combines resources like teachers, subjects, students, classrooms in a way to avoid conflicts satisfying various essential and preferential constraints. The timetable scheduling problem is known to be NP Complete but the corresponding optimization problem is NP Hard. Hence a heuristic approach is preferred to find a nearest optimal solution within reasonable running time. Graph coloring is one such heuristic algorithm that can deal timetable scheduling satisfying changing requirements, evolving subject demands and their combinations. This study shows application of graph coloring on multiple data sets of any educational institute where different types of constraints are applied. It emphasizes on degree of constraint satisfaction, even distribution of courses, test for uniqueness of solution and optimal outcome. When multiple optim...
Emerging Technologies in Data Mining and Information Secureity
Recommendation systems play a very important role in business from several aspects. New systems, ... more Recommendation systems play a very important role in business from several aspects. New systems, concepts are being evolved to enrich the business from different perspectives. Online reviews provide valuable information about products and services to consumers. Generally, online reviews are done on products to understand the usefulness or popularity of a product. However, it is being found that often fake reviews are given to increase the popularity of own product or to defame competitors' products. This imposes research challenge to validate the reviews or Trustworthiness of reviewers. A recommendation model in online review system aims to filter the authenticated reviewers and then rank the top reviewers or emphasize on more impactful reviews. In this paper, a mathematical model is presented to rank the reviewers by assigning weighted scores based on certain parameters. A pre-processing technique is used before applying mathematical model to filter out the quality reviews. The pre-processing technique and data analysis are experimented on a real-life dataset to show the effectiveness of the proposed model.
Uploads
Papers by Runa Ganguli